Real-time Platform Research Articles

Development of a new tool to detect Melampsora medusae f.sp. tremuloidae causing rust disease on Populus tremuloides.

Melampsora medusae f. sp. tremuloidae is a quarantine organism for the EU. In North America, this fungus causes rust disease on Populus tremuloides. In Europe, Populus tremula, an aspen closely related to P. tremuloides, is widespread and plays an important ecological role. Introduction of M. medusae f. sp. tremuloidae into Europe could be a major risk if this forma specialis could evolve and become virulent on P. tremula. To date no PCR-based assay exists to specifically detect M. medusae f. sp. tremuloidae. In this study, a sensitive and specific real-time PCR assay has been developed based on the 28S rDNA. The assay proved to be reliable using many real-time PCR kits and platforms. It can be used to monitor the introduction and the spread of M. medusae f. sp. tremuloidae in the context of phytosanitary regulations.

Performance Assessment of a Multiplex Real-Time PCR Assay for Detection of Viruses Causing Respiratory Tract Infections

Objectives: Following the COVID-19 pandemic, global epidemiological trends demonstrate a return to pre-pandemic levels of respiratory syncytial virus (RSV) and influenza (Flu) A/B viruses. For the appropriate clinical management of viral infections, reliable and timely diagnosis is crucial. The clinical presentation of these respiratory viral infections shows significant overlaps; thus, the syndromic diagnosis of these infections becomes challenging. The goal of this study was to compare the performance of three multiplex real-time PCR-based platforms for the detection of SARS-CoV-2, Flu A, Flu B, and RSV. Materials and Methods: A retrospective study was performed on 200 de-identified nasopharyngeal and oropharyngeal specimens. All samples were tested simultaneously on three PCR-based platforms for the detection of SARS-CoV-2, Flu A, Flu B, and RSV: HealthTrackRx’s real-time PCR Open Array® respiratory panel, TrueMark™ SARS-CoV-2, Flu A, Flu B, RSV Select Panel, and BioFire® RP2.1 Panel. The positive and negative predictive value of each test was evaluated at a 95% confidence interval. Results: Among the 200 tested samples, the TrueMark™ and OpenArray® laboratory-developed tests (LDTs) showed a 100% concordance for the detection of SARS-CoV-2, Flu A, Flu B, and RSV. Overall agreement of 100% was observed for nasopharyngeal samples between the laboratory-developed tests and FDA-approved BioFire® RP2.1 Panel. Diagnostic results for these four respiratory viruses, in clinical samples, between the LDTs and the FDA-approved comparator demonstrated full concordance. Conclusions: Respiratory viral infections represent one of the major global healthcare burdens. Consequently, the accurate detection and surveillance of these viruses are critical, particularly when these viruses are known to co-circulate. The excellent performance and full concordance of the LDTs, with the BioFire® Respiratory RP2.1 panel, in detecting SARS-CoV-2, Flu A, Flu B, and RSV shows that these tests can be confidently implemented for the clinical testing of respiratory viral infections.

The Simulator of China General Nuclear Power Group (CGNPC) Delingha 50 MW Parabolic Trough Solar Thermal Power Plant

CGN Delingha 50MW parabolic trough solar power plant is China's first commercial trough solar power station. There are 190 heat collecting loops in the solar field of the power plant. Due to the mutual coupling of heat collecting loops layout location, environmental conditions, medium flow, working medium physical property changes and other factors, the hydrodynamic characteristics of the solar field are complex. In practical operation, balancing the flow of the heat absorbing medium in each loop of the large-scale trough solar thermal power plant and stabilizing the outlet medium temperature are difficult problems. Taking CGN Delingha 50MW parabolic trough solar power plant as the research object, this paper adopts the idea of modular modeling to establish the mathematical model of the main equipment in different systems of the power plant, to build the dynamic model of its solar field, steam generation system, heat storage system and steam turbine system. Based on the parallel computing function of the real-time dynamic simulation platform STAR-90, the real-time coupling calculation between different systems is realized. The error of the built model in steady-state is less than 2%, and the dynamic results of the model are in good agreement with the actual operation data. The modeling method in this paper is universal and can be a reference for dynamic modeling of other large-scale trough solar thermal power plants.

Residual transmission of HIV infection from mother to child in the Atlantic and littoral departments in Benin

BackgroundThe implementation of the WHO’s 2015 recommendations in Benin, requires an assessment of the progress made over time in preventing the transmission of the infection to exposed-infants, and the identification of its determinants.MethodsThis was a retrospective study of HIV-1 exposed-infants who underwent PCR between the 6th and 8th weeks of life. Early diagnostic tests were performed using the Abbott m2000 RealTime platform. Comparison of proportions tests (analysis of the significance of the difference in prevalence) with an error threshold of 5% were used to assess the determinants of the transmission. Statistical analysis was performed using R statistical software, version 4.1.3.0.ResultsA total of 5,312 infants benefited from early diagnosis by PCR between 2016 and 2021. Among them, 52% are males, tritherapy before pregnancy was the majority treatment used by mothers (30.6%) and monotherapy that of newborns (70%). Mixed breastfeeding is the feeding method with the highest prevalence. The overall transmission rate was 3.4% over the six years. The highest prevalence was achieved in 2018 (4.2%) and the lowest in 2021 (2.7%). The prevalence was lower when mothers were on tritherapy before pregnancy. The determinants of transmission were: mixed breastfeeding, lack of treatment in mothers (22.4%), lack of treatment in infants (19.7%), undefined treatments or absence of treatment in the mother-child pair.ConclusionThis study shows the contribution over time of the PMTCT program to reducing HIV transmission among exposed-infants and also underlines the need for proper conduct of treatment in any women of childbearing age.

Operational reliability and non-deterministic resilience estimation of active distribution network incorporating effect of real-time dynamic hosting capacity

Active distribution networks are increasingly recognized essential for achieving sustainable development goals. Traditionally, hosting capacity was considered as a static measure for planning distributed energy resources integration. This work introduces the concept of dynamic hosting capacity, which recurrently re-evaluates hosting capacity in response to erratic modern grid conditions. The introduction of dynamic hosting capacity facilitated testing variations of power injection from minimum to 100 %, sustaining power system governing parameter limits. This embarked the need of operational reliability assessment and enhancing situational awareness for optimum power injection and balance. To achieve operational reliability analysis based on dynamic hosting capacity, hybrid probability distribution function-based Monte Carlo simulation is proposed. This resulted in 85–90 %. improvisation of solar photovoltaic generation and load alignment, as this methodology provides comprehensive and accurate assessment of system performance under diverse uncertainties. The framework's validation includes projection of time-varying operational reliability indices, over time independent reliability indices i.e., dynamic loss of load probability, dynamic loss of load expectation, dynamic loss of load duration, dynamic loss of load frequency, dynamic grid margin, and dynamic grid dependency. This resulted in 30 % improvement in assessment of grid margin, facilitating reliable uncertainty handling competence. Additionally, expectation maximization algorithm is proposed to evaluate non-deterministic resilience due to ambiguities associated with solar photovoltaic distributed energy resources. The non-deterministic resilience assessment testified 80 % bounce-back rate, demonstrating better adaptability and robustness. The entire analysis is conducted in MATLAB, validated using Typhoon Hardware-in-Loop real-time platform, and compared with existing literatures to demonstrate its effectiveness.

Calibration modeling of drilling fluid rheological parameters in variable temperature environment based on support vector machine.

Traditional measurement of drilling fluid rheological parameters suffers from significant lag due to the inability of the instruments to promptly capture real-time parameters of the drilling fluid. These measurement models are typically constructed based on fixed temperature conditions and empirical formulas, rendering them inadequate for complex temperature gradient environments. Consequently, this limitation results in increased prediction errors, severely compromising the precise monitoring of drilling fluid performance. Aiming at the problems of low accuracy and poor stability of drilling fluid measurements under variable temperature conditions, a support vector machine-based calibration model for drilling fluid rheological parameters in a variable temperature environment is proposed in this paper. First, the measurement principle of the double-tube differential pressure rheology real-time measurement device is analyzed. The relationship between shear stress and shear rate is then established using differential pressure sensor and flow rate data. Utilizing the gray wolf optimization algorithm to optimize the kernel function weights and parameters, an SVM-based calibration model for predicting drilling fluid rheology correction parameters is constructed. Finally, a real-time monitoring platform for drilling fluid is developed. Experimental results show that the maximum relative errors for the predictions of apparent viscosity, plastic viscosity, and yield point are within ±5%, with coefficients of determination (R2) all greater than 0.95. These results validate the effectiveness of the proposed method in accurately monitoring the rheological performance of drilling fluids.

Intraoperative guidance of pancreatic cancer resection using a toll like receptor 2 targeted fluorescence molecular imaging agent.

To increase the achievement of negative R0 surgical margins and increase the low survival rates of pancreatic cancer, improvements in assessing tumor margins during surgical resections are needed. This can be accomplished by using pancreatic cancer-targeted fluorescence molecular imaging agents to intraoperatively detect tumor margins in real-time. Since toll like receptor 2 (TLR2) is broadly expressed among many cancer types including pancreatic adenocarcinomas, a high-affinity TLR2-targeted fluorescence molecular imaging agent (TLR2L-800) was developed. We investigate the potential for increased survival by employing real-time intraoperative tumor detection in a preclinical orthotopic human pancreatic xenograft tumor model using TLR2L-800. Three cohorts of nude mice bearing orthotopic human pancreatic xenograft tumors were intravenously injected with TLR2L-800. At 24 h postinjection, one cohort underwent in vivo fluorescence-guided surgical removal of tumors using a real-time fluorescence imaging platform, a second cohort underwent visible light surgery, and a third cohort did not undergo surgery. A fourth, non-tumor-bearing cohort was administered TLR2L-800 with no surgery. At 41 d post-surgery, the survival rates were 53% for the fluorescence-guided surgery group and 0% for both the visible light surgery group and the tumor-bearing no surgery group. The overall 200 d survival rate of 35% for the fluorescence-guided surgery group was significant compared to 0% for the visible light surgery group (p-value=0.0018). This study demonstrates the potential of increasing disease-free survival for patients with pancreatic cancer by increasing the attainment of R0 margins using a novel tumor-targeted lipopeptide ligand-based fluorescence molecular imaging agent, TLR2L-800, during real-time fluorescence-guided surgery.

Development of a Machine Learning Framework for Real-Time PMI (Post-Mortem Interval) Estimation in Field Forensics

Accurate estimation of the Post-Mortem Interval (PMI) is critical in forensic investigations, aiding in determining the time of death. However, traditional PMI estimation methods, often reliant on physiological observations and environmental factors, face significant limitations in accuracy and efficiency, especially in field conditions. This paper presents the development of a machine learning (ML) framework designed for real-time PMI estimation, integrating multimodal sensor data to address the challenges encountered in field forensics. Our framework utilizes environmental and physiological features, including body temperature, ambient humidity, and biochemical decomposition markers, to predict PMI with high precision. The ML model, trained on historical forensic data, is deployed on a real-time processing platform, enabling rapid analysis and decision-making in resource- constrained environments. The system is optimized for field operations, incorporating low-power hardware and edge computing capabilities to provide forensic investigators with reliable PMI estimates on-site. Through a series of controlled experiments simulating forensic scenarios, our framework demonstrates a significant improvement in PMI accuracy compared to traditional methods, while maintaining low latency for real-time applications. This research highlights the potential of machine learning to revolutionize forensic practices, offering a scalable and adaptive solution for time-sensitive investigations. Here are some relevant keywords for the development of a machine learning framework for real-time PMI (Post- Mortem Interval) estimation in field forensics: Keywords: Field Forensics, Real-Time Machine Learning, Body Decomposition Stages, Machine Learning in Forensic Science, Artificial Intelligence for PMI Analysis, Sensor Data in PMI Estimation, Deep Learning for PMI Estimation, Automated Forensic Analysis, Data Acquisition in Field Forensics.

Getting rapid diagnostic test data into the appropriate hands by leveraging pharmacy staff and a clinical surveillance platform: a case study from a US community hospital

Abstract Objectives To outline the procedural implementation and optimization of rapid diagnostic test (RDT) results for bloodstream infections (BSIs) and to evaluate the combination of RDTs with real-time antimicrobial stewardship team (AST) support plus clinical surveillance platform (CSP) software on time to appropriate therapy in BSIs at a single health system. Methods Blood culture reporting and communication were reported for four time periods: (i) a pre-BCID [BioFire® FilmArray® Blood Culture Identification (BCID) Panel] implementation period that consisted of literature review and blood culture notification procedure revision; (ii) a BCID implementation period that consisted of BCID implementation, real-time results notification via CSP, and creation of a treatment algorithm; (iii) a post-BCID implementation period; and (iv) a BCID2 implementation period. Time to appropriate therapy metrics was reported for the BCID2 time period. Results The mean time from BCID2 result to administration of effective antibiotics was 1.2 h (range 0–7.9 h) and time to optimal therapy was 7.6 h (range 0–113.8 h) during the BCID2 Panel implementation period. When comparing time to optimal antibiotic administration among patients growing ceftriaxone-resistant Enterobacterales, the BCID2 Panel group (mean 2.8 h) was significantly faster than the post-BCID Panel group (17.7 h; P = 0.0041). Conclusions Challenges exist in communicating results to the appropriate personnel on the healthcare team who have the knowledge to act on these data and prescribe targeted therapy against the pathogen(s) identified. In this report, we outline the procedures for telephonic communication and CSP support that were implemented at our health system to distribute RDT data to individuals capable of assessing results, enabling timely optimization of antimicrobial therapy.

Pollen detection through integrated microfluidics and smartphone-driven deep learning systems

The presence of atmospheric particulates, including bioaerosols such as pollen, significantly impacts air quality and poses potential health risks. Therefore, accurate detection and categorization of these particulates are crucial for evaluating their health implications. This study introduces an innovative, automated system for analyzing atmospheric particulates, combining microfluidic technology with a smartphone-based photonic detection platform. The system integrates a miniaturized optical microscopy module and a consumer-grade high-frame-rate camera, creating an affordable, accessible solution priced at only 50 USD. Such cost-effectiveness promotes the widespread adoption of the technology and facilitates community-driven environmental monitoring. The study developed a compact, rapid, real-time, and continuous air micro-particle sampling and detection platform. The integration of an automatic collection device with a microfluidic detection chip enables continuous sampling and detection. A lightweight object detection network, designated as “PollenDet,” was trained, achieving a mean average precision (mAP) of 94.6%. This technology provides essential insights into the dynamics of airborne particulates, thereby enhancing our understanding of their overall impact.

Data Monetization Strategies: The Construction Industry Market Analysis

Data monetization is leveraging data to obtain economic benefits. In the context of Big Data Analytics (BDA), data serves as a fundamental asset, enabling the transformation into valuable insights throughout various construction phases. Unfortunately, there is lack of studies focusing on data monetization within the construction industry, given the unique supply and demand characteristics among construction stakeholders. This study aims to address the objective of identifying data monetization strategies applicable to the construction industry, particularly for data sellers and data buyers. Data was gathered through a quantitative survey among 100 construction practitioners in Malaysia, encompassing developers, contractors, consultants, government agencies, technology providers, and academia. Respondents were further categorized into data sellers and data buyers. The data were analyzed using mean analysis, t-test and Spearman's rank correlation coefficient. The study identified six data monetization strategies, comprising 19 determinants. The analysis revealed high preference on all data monetization strategies and moderate differences in the preferred strategies between data sellers and buyers. Significant differences were found in 3 determinants which are (1) trading data to facilitate decision-making, (2) trading data for construction reports, benchmarks, and indices, and (3) selling visualized data on real-time platforms. The t-test indicated that data sellers are more inclined towards the three strategies for effective monetization. Furthermore, Spearman’s correlation coefficient revealed the 3 determinants also positively influence another 3 determinants of (1) data wrapping to reflect better service from data provider, (2) buying raw data with its inherit information and (3) monetizing internal data to optimize organization’s work process. The insights enable stakeholders to implement mechanisms that foster data monetization within project cultures accelerating BDA undertakings. Future recommendations include using larger sample sizes to enhance generalizability and to explore more areas such as construction contracts, cost, health, and safety.

Association between City-Level Particulate Matter Exposure and Frailty among Middle-Aged and Older Adults in China

Introduction: The effects of exposure to particulate matter and frailty, as well as its exposure-response relationship, have not been effectively explored. This study aimed to explore the association between long-term exposure to particulate matter and frailty state and each dimension in Chinese middle-aged and older adults, in addition to the exposure-response relationship. Methods: The data were obtained from the National Urban Air Quality Real-Time Dissemination Platform and China Health and Retirement Longitudinal Study (CHARLS). Frailty was measured by a frailty index containing 39 indicators. Annual averages of seven pollutants were calculated from hourly monitoring data. We used multilevel regression modeling to explore the association between long-term exposure to particulate matter and frailty. Meanwhile, we explored the exposure-response relationship based on a multilevel generalized summation model. We performed a sensitivity analysis using a multi-pollution model and a quantile-based g-computation (QGC) model. Results: A total of 15,611 participants were included in the analysis. We find that long-term exposure to PM2.5 was associated with an increased risk of pre-frailty and frailty (all p < 0.05). PMc and PM10 exhibited similar associations. The exposure-response relationship between PM2.5 showed a linear relationship, whereas the exposure-response relationship between PM10, PMc showed a nonlinear relationship. Elevated PM2.5 concentrations showed significant positive associations with the number of chronic disease score, IADL score, and functional limitation status score (all p < 0.05). PM10 and PMc showed similar positive correlations. These results remained robust after sensitivity analyses using a multi-pollution model and QGC model. Conclusion: Chronic exposure to particulate matter was significantly associated with increased risk of frailty. The exposure-response relationship between PM2.5 concentration and frailty showed a linear relationship, and the exposure-response relationship between PM10 and PMc showed a nonlinear relationship. Exposure to a mixture of pollutants carried a higher risk of frailty than exposure to a single pollutant.

A multiplexed real‐time PCR assay for simultaneous quantification of human immunodeficiency virus and Hepatitis B virus for low‐and‐middle‐ income countries

Due to shared routes of transmission, including sexual contact and vertical transmission, HIV-HBV co-infection is common, particularly in sub-Saharan Africa. Measurement of viral load (VL), for both HIV and HBV, plays a critical role for determining their infectious phase and monitoring response to antiviral therapy. Implementation of viral load testing in clinical settings is a significant challenge in resource-limited countries, notably because of cost and availability issues. We designed HIV and HBV primers for conserved regions of the HIV and HBV genomes that were specifically adapted to viral strains circulating in West Africa that are HIV-1 subtype CRF02AG and HBV genotype E. We first validated two monoplex qPCR assays for individual quantification and, then developed a multiplex qPCR for simultaneous quantification of both viruses. HIV RNA and HBV DNA amplification was performed in a single tube using a one-step reverse transcription-PCR reaction with primers and probes targeting both viruses. Performance characteristics such as the quantification range, sensitivity, and specificity of this multiplex qPCR assay were compared to reference qPCR tests for both HIV and HBV viral load quantification. The multiplex assay was validated using clinical samples from co- or mono-infected patients and gave comparable viral load quantification to the HIV and HBV reference test respectively. The multiplex qPCR demonstrated an overall sensitivity of 71.25 % [68.16–74.3] for HBV and 82 % [78.09–85.90] for HIV and an overall specificity of 100 % [94.95–100] for both viruses. Although the overall sensitivities of the HIV and HBV assays were lower than the commercial comparator assays, the sensitivity in the clinical decision range of >1000 copies/mL for HIV was 80 % [71.26–88.73] and >1000 IU/mL for HBV was 100 % [95.51–100] which indicates the test results can be used to guide treatment decisions. This in-house developed multiplex qPCR assay represents a useful diagnostic tool as it can be performed on affordable "open" real-time PCR platforms currently used for HIV or SARS-Cov-2 infection surveillance in Mali.

Cytology Smears can be Effectively Used on Two Different Fully Automated Real-Time PCR Platforms That Have Been Validated for Use Only with FFPE Tissue

Cytology Smears can be Effectively Used on Two Different Fully Automated Real-Time PCR Platforms That Have Been Validated for Use Only with FFPE Tissue

Positive sequence component based directional relaying for lines integrated with solar photovoltaic plant

Different fault current characteristics of converter interfaced solar photovoltaic plants as compared to the synchronous generator based sources impose challenges to the commonly used directional relaying methods. In this work, the issues with phasor based directional relay algorithms are analysed for the solar plant connected lines. A directional relaying method is proposed for such connectivity using the phase angle difference between positive sequence voltage and the line current measured at the local bus relay. Using PSCAD/EMTDC simulations the proposed method is tested for solar plant connected 39 bus system and validated using field data. The accuracy of the proposed method is also evaluated on a real-time simulation platform. Comparative analysis with the conventional directional relaying methods reveals the superiority of the proposed method.

IRTIDP: A simple integrated real-time isolation and detection platform for small extracellular vesicles Glypican-1 in pancreatic cancer patients

Glypican-1 (GPC-1) protein-positive small extracellular vesicles (GPC-1+-sEV) have been proposed as potential biomarkers for early diagnosis of pancreatic cancer. In this study, we present an integrated real-time isolation and detection platform (IRTIDP) to capture and analyze GPC-1+-sEV directly from sera of pancreatic cancer patients. First, CD63 antibody-modified metal-organic framework (MOF) materials were utilized to enrich sEVs with a capture efficiency of 93.93 %. Second, a SERS probe was constructed by Raman reporter 4-MBA and GPC-1 antibody modified SERS active silver nanoparticles (AgNPs), which formed a sandwich complex structure of "MOFs@GPC-1+-sEV@AgNPs-4-MBA" with MOFs-enriched sEVs. The IRTSDP can complete the capture and detection process within 35 min, with a detection limit for 1 GPC-1+-sEV/μL, and linear range between 105∼109 GPC-1+-sEV/mL. Furthermore, this approach has been applied to quantify serum sEV GPC-1 in clinical pancreatic cancer patients. Based on the SERS intensity analysis, pancreatic cancer patients can be distinguished from pancreatic cystadenoma patients and healthy individuals effectively using this innovative platform that provides highly specific and sensitive means for early diagnosis of pancreatic cancer as well as other tumor types.

Modeling for sustainable groundwater management: Interdependence and potential complementarity of process-based, data-driven and system dynamics approaches

Groundwater systems are vast natural water reservoirs used to support human water demands and ecosystem services. Various modeling approaches have been developed to help manage these complex highly-dynamic systems. This paper discusses the strengths and limitations of three modeling approaches, namely: process-based, data-driven and system dynamics modeling. For demonstration purposes, the three modeling approaches are applied to the Konya Closed Basin, a large agricultural region with semi-dry climate located in central Turkey. Process-based modeling is grounded in the theory-based representation of the governing processes but is somewhat limited by the computational effort and the difficulty of defining the required input parameters that characterize the heterogeneous aquifer system. Process-based models are shown to be powerful tools for resource management purposes provided climatic and water demand scenarios are accurately defined. Data-driven models are efficient tools for the management of groundwater resources but are highly dependent on the availability of large training data sets encompassing the spectrum of possible system responses. The high efficiency of surrogate modeling approaches makes them ideal tools for incorporation into applications such as real-time decision support systems and digital twin platforms. System dynamics modeling examines the groundwater exploitation problem within a socio-economic context that involves multiple stakeholders and their decision making. It combines groundwater flow models with socio-economics and endogenous decision rules to conduct scenario analysis and support policy development. The analyses and model demonstrations presented in this paper underscore the interconnectedness and complementarity of these three modeling approaches and the need for more integrated use of these modeling approaches for enhanced multi-sectoral management of groundwater systems.

Initial treatment experience obtained with the real-time predictive motion tracking radiotherapy platform Synchrony: A pilot study.

This pilot study presents initial experience obtained with a real-time predictive motion tracking platform called Synchrony mounted on the Radixact radiotherapy device. Synchrony radiotherapy treatments were offered as an alternative to surgical excision for primary pulmonary carcinomas as well as in dogs in a suspected oligometastatic disease state. All dogs were treated with three fractions of 8 Gy. Six dogs with pulmonary targets were successfully treated, while we were unable to treat abdominal targets with implanted fiducials. Cranial targets showed minimal movement, while targets located adjacent to the diaphragm showed a large amplitude of movement. No acute or late clinically apparent side effects were noted in any of the dogs that received radiation therapy. A strong partial response with minimal pneumonitis was seen in follow-up imaging of the one dog where imaging was available. Synchrony motion tracking will continue to be investigated for efficacy.

DC fault current clearance coordinated control strategy for DC grid with hybrid MMC

For DC grid based on the hybrid modular multilevel converter (MMC), the traditional DC fault current clearance scheme takes a long time and greatly affects the active power transmission. A novel DC fault current clearance coordinated control strategy is proposed, which can quickly interrupt the fault current and reduce the impact of DC fault on the DC grid and AC system. For fault-line connected MMC (FLMMC), a negative DC voltage control is employed, which can improve the current attenuation speed and ensure reliable fault isolation. For non-fault-line connected MMC (NFLMMC), the active current-limiting control (ACLC) based on the virtual reactor is adopted, which can reduce the current flow to the fault location and further shorten fault isolation time. To reduce the impact on the AC system during the DC fault, a short-time active power support control is designed. Finally, a four-terminal DC grid simulation model is built based on the RT-LAB OP5600 real-time digital simulation platform. The simulation results show that the proposed coordinated control strategy for the DC grid can quickly clear DC fault current, shorten DC fault isolation time, and strengthen active power support capability.

A self-powered sensor based on hollow bimetallic sulfide and double-strand TSDR amplification achieves sensitive miRNA-21 detection

MicroRNA (miRNA) sensitive detection is significant for cancer diagnosis. Currently, self-powered biosensors in clinical miRNA detection show the advantages of no external power supply, mild reaction conditions, low cost and portability. In this work, the bimetallic sulfide heterojunction hollow nanocubes are prepared as the substrate material, which can significantly improve the electrochemical reaction rate and increase the enzyme load. Meanwhile, we design the independent duplex-chain assisted toehold mediated strand displacement reaction (TSDR) strategy. The two substable double chains trigger a continuous chain displacement reaction, and the current signal amplification is realized based on molecular recognition. The constructed sensor has a linear response range of 0.05–10000 fM and a detection limit (LOD) of 8.3 aM. In addition, we use commercial apps to present signals, fundamentally changing the signal output mode of electrochemical sensors, and facilitating the construction of a portable real-time sensor platform. The self-powered biosensor has the characteristics of good biocompatibility, real-time monitoring and economic efficiency in miRNA detection, which provides a new idea for sensitive detection of miRNA. Moreover, it has far-reaching reference significance for the development of other sensing technologies, which is expected to extend from the medical field to environmental monitoring, food safety and other fields.